High Renewable Energy Integration Research Articles

Assessment of the Renewable Energy Consumption Capacity of Power Systems Considering the Uncertainty of Renewables and Symmetry of Active Power

The rapid growth of renewable energy presents significant challenges for power grid operation, making the efficient integration of renewable energy crucial. This paper proposes a method to evaluate the power system’s capacity to accommodate renewable energy based on the Gaussian mixture model (GMM) from a symmetry perspective, underscoring the symmetrical interplay between load and renewable energy sources and highlighting the balance necessary for enhancing grid stability. First, a 10th-order GMM is identified as the optimal model for analyzing power system load and wind power data, balancing accuracy with computational efficiency. The Metropolis–Hastings (M-H) algorithm is used to generate sample spaces, which are integrated into power flow calculations to determine the maximum renewable energy integration capacity while ensuring system stability. Short-circuit ratio calculations and N-1 fault simulations validate system robustness under high renewable energy integration. The consistency between the results from the M-H algorithm, Gibbs sampling, and Monte Carlo simulation (MCS) confirms the approach’s accuracy.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman’s rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer’s components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs.

Advancing Distance Relaying Reliability: S Transform Solution for Renewable Energy-Infused Smart Grid

Distance relaying is a crucial component in safeguarding transmission lines globally. However, the presence of Sub-Synchronous Resonance (SSR) introduces challenges, as oscillations in voltage and current magnitudes adversely affect the performance of conventional distance relays. Detecting faults accurately during these oscillations poses a significant challenge for traditional relaying mechanisms. This paper addresses the issue of SSR-induced maloperations in distance relays, particularly in the context of the increasing penetration of renewable energy resources in smart grids. The integration of wind power into the grid generates sub-synchronous oscillations, leading to protection issues. In response, a novel solution leveraging the S transform, a modern signal processing technique, is proposed to enhance the robustness of distance relaying under such challenging conditions. The developed algorithm is implemented in MATLAB and rigorously tested on the First Benchmark Model of sub-synchronous resonance. Simulation of the model is conducted in the PSCAD simulation software, and the results are thoroughly validated in MATLAB. The study demonstrates the efficacy of the S transform-based approach in accurately identifying faults and mitigating maloperations caused by SSR, thereby improving the reliability of distance relaying in smart grids with high renewable energy integration.

Design and implementation of an AI-based & IoT-enabled Home Energy Management System: A case study in Benguerir — Morocco

Home Energy Management Systems (HEMS) are of great importance today and have attracted a great deal of interest from both, academic researchers, and industrial engineers. These systems are considered as the intersection point between the smart grid and the smart home. Indeed, they can communicate with the user, home appliances, on-site generation sources, storage devices, and the grid operator to make dynamic decisions enabling intelligent and efficient energy management.This work aims to design and implement a smart HEMS that ensures renewable energy integration and energy efficiency improvement in residential buildings. The system designed in this work has been implemented as part of a pilot project in a testbed house of the Smart Campus — Green & Smart Building Park, Benguerir, Morocco.The proposed HEMS framework is based on two control strategies that work jointly: the first concerns the scheduling and control of power dispatch among generation, consumption, and storage agents (Supply-Side Management), while the second concerns the scheduling and control of flexible appliances for optimal load profile modulation (Demand-Side Management). The management of energy flows is based on grid electricity price, forecasting data (PV generation and weather conditions) and user preferences. The two designed control strategies are combined into an AI-based multi-objective optimization algorithm that minimizes costs and maximizes comfort level simultaneously.The obtained results validate the effectiveness of the proposed algorithm and indicate that it would significantly increase the penetration of PV energy for self-consumption and reduce electricity costs, while ensuring a proper compromise between monetary spending and comfort level. The large-scale implementation of such systems would help decarbonize residential energy sector through higher renewable energy integration and energy efficiency improvement in buildings.

Embedding power system’s reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050

The aim of this article is to take into account short-term power grid operation conditions in long-term prospective analysis in the case of France. It is the first time that the integration of system adequacy and transient stability has been achieved in prospective studies for an electro intensive country, following studies conducted on Reunion Island. The methodology relies on a quantitative assessment of the French power sector’s reliability through an endogenous definition of a reliability indicator related to kinetic reserves into an Energy System Optimization Model (ESOM), TIMES-FR model. The result gives an overview of how the stability of the grid is maintained with an increasing share of renewables using additional back-up and flexible options. We observe that it is technically possible to achieve around 65% of Variable Renewable Energy sources (VREs) in the installed capacity without impairing the reliability of the system. In more detail, the maximum VRE in total hourly power production that complies with the reliability constraint was assessed as around 84% in the 100 EnR scenario. However, in order to guarantee this system reliability, the cumulated new installed capacity, in a scenario with 100% renewable energy sources (RES) in the power mix, would represent the double of the Business-As-Usual (BAU) scenario over the period 2013–2050. Therefore, major upstream planning would be needed, and that more flexible options i.e. demand-response, storage technologies and interconnections or substitute or additional plants should be considered to satisfy the reliability constraint at any time by providing extra inertia to the system. This modelling exercise shows the importance of power exchanges with neighbours with higher share of RE in the power production.

Reflection on opportunities for high penetration of renewable energy in China

Increasing penetration of renewable energy (RE) is expected to be an essential component for China to achieve goals in mitigating global climate change and domestic air pollution. Although China has experienced rapid growth of RE over the past two decades, problems restricting further development of RE occur, among which high curtailment rate of wind and solar power raises great attention. This paper reflects on the challenges and opportunities of high penetration of RE from both technical and market perspectives in China based on literature review. In particular, it emphasizes technical options to address the inherent variability of RE from three layers, namely using interconnected geographically dispersed sources to reduce random variance, increasing storage capacity to deal with minute‐to‐day imbalance, and applying backup capacity for seasonally mismatch. From the market perspective, the paper analyzes the needs of shifting from central planning to market‐based dispatch, introducing markets for ancillary services, and having a larger and more precise market. Meanwhile, our paper further points out that opportunities for high RE integration may rely on deep electrification of transportation and building sectors, which are also the foci for future air pollution control in China. It is feasible for China to achieve a high if not 100% RE system, but first these technical and market challenges have to be addressed in the process of adjusting its energy structure.This article is categorized under: Energy Systems Analysis > Climate and Environment Energy and Climate > Climate and Environment

Potential distributions of electric vehicle secondary used batteries for frequency regulation in Europe

The number of electric vehicles (EVs) has been rapidly increasing, leading to the importance to predict the most beneficial way to integrate them with electrical grids. Continuous charging and discharging of EVs make their batteries degrade, resulting in a need for them to be replaced. Therefore, many researchers have been discussing possible utilization of the secondary used batteries, as their remaining capacities can assist in grid services, including peak load shaving and providing ancillary services for the grids. In this study, the future supply of secondary used batteries from the selected European countries is evaluated. In addition, a possible distribution from one country to another based on frequency regulation needs is also observed. The results show a remarkable relation between grids with high renewable energy integration and need for batteries for frequency regulation.

Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration

Renewable energy sources (RESs), such as wind and solar generations, equip inverters to connect to the microgrids. These inverters do not have any rotating mass, thus lowering the overall system inertia. This low system inertia issue could affect the microgrid stability and resiliency in the situation of uncertainties. Today’s microgrids will become unstable if the capacity of RESs become larger and larger, leading to the weakening of microgrid stability and resilience. This paper addresses a new concept of a microgrid control incorporating a virtual inertia system based on the model predictive control (MPC) to emulate virtual inertia into the microgrid control loop, thus stabilizing microgrid frequency during high penetration of RESs. The additional controller of virtual inertia is applied to the microgrid, employing MPC with virtual inertia response. System modeling and simulations are carried out using MATLAB/Simulink® software. The simulation results confirm the superior robustness and frequency stabilization effect of the proposed MPC-based virtual inertia control in comparison to the fuzzy logic system and conventional virtual inertia control in a system with high integration of RESs. The proposed MPC-based virtual inertia control is able to improve the robustness and frequency stabilization of the microgrid effectively.

Higher renewable energy integration into the existing energy system of Finland – Is there any maximum limit?

Finland is to increase the share of RES (renewable energy sources) up to 38% in final energy consumption by 2020. While benefiting from local biomass resources Finnish energy system is deemed to achieve this goal, increasing the share of other intermittent renewables is under development, namely wind power and solar energy. Yet the maximum flexibility of the existing energy system in integration of renewable energy is not investigated, which is an important step before undertaking new renewable energy obligations. This study aims at filling this gap by hourly analysis and comprehensive modeling of the energy system including electricity, heat, and transportation, by employing EnergyPLAN tool. Focusing on technical and economic implications, we assess the maximum potential of different RESs separately (including bioenergy, hydropower, wind power, solar heating and PV, and heat pumps), as well as an optimal mix of different technologies. Furthermore, we propose a new index for assessing the maximum flexibility of energy systems in absorbing variable renewable energy. The results demonstrate that wind energy can be harvested at maximum levels of 18–19% of annual power demand (approx. 16 TWh/a), without major enhancements in the flexibility of energy infrastructure. With today's energy demand, the maximum feasible renewable energy for Finland is around 44–50% by an optimal mix of different technologies, which promises 35% reduction in carbon emissions from 2012's level. Moreover, Finnish energy system is flexible to augment the share of renewables in gross electricity consumption up to 69–72%, at maximum. Higher shares of RES calls for lower energy consumption (energy efficiency) and more flexibility in balancing energy supply and consumption (e.g. by energy storage).